Proceedings of the 2013 Winter Simulation Conference
R. Pasupathy, S.-H. Kim, A. Tolk, R. Hill, and M. E. Kuhl, eds
DECISION MAKING ON MANUFACTURING SYSTEM FROM THE PERSPECTIVE OF
MATERIAL FLOW COST ACCOUNTING
Hikaru Ichimura
Soemon Takakuwa
Nagoya University
Graduate School of Economics and Business Administration
Furo-cho, Chikusa-ku, Nagoya, Aichi, 464-8601, JAPAN
ABSTRACT
Recently, significant research interest has been focused on environmental management aimed at the sustainable development of enterprises and society while decreasing the impact of such development on the
environment. Japanese companies have been developing a variety of approaches and strategies. Material
flow cost accounting (MFCA) has been proposed as a generally applicable indicator of growth potential
and corporate environmental impact. Many companies that have introduced MFCA could recognize previously unnoticed losses. In addition, MFCA is useful as a tool to evaluate environmental impact and
draft improved, more cost-efficient manufacturing plans. This paper demonstrates that companies that introduce MFCA can improve decision-making procedures and advantageously alter their manufacturing
methods in a manner that differs from the inventory reduction idea based on the traditional Toyota production system.
1
INTRODUCTION
In recent years, social concern regarding environmental issues has been increasing. Thus, firms have begun to pay increased attention to environmental management, and the importance of environmental information is larger. It has become increasingly important to address how corporate management can address environmental impact concerns. MFCA is effective and represents one type of environmental
management accounting that has already been introduced in several firms. The goal of this approach is to
minimize material costs, the costs of processing waste material and energy consumption.
In this study, previously unrecognized losses are revealed using MFCA. In chapter three, the MFCA
concept and its effectiveness are described. MFCA has been introduced at one factory. An examination of
this company’s MFCA use indicates that the method can improve a production system based on the Toyota production system (TPS).
A new idea derived from an MFCA analysis is reproduced in a simulation. The idea is that the quantity of raw materials that is not consumed in the manufacturing process is reduced instead of allowed to increase the existing stock or raw materials, contrary to the traditional principles of TPS inventory reduction.
The production process model was designed using ARENA simulation software. Two types of simulation
models were created which is an AS-IS model based on current production theory and a TO-BE model
based on the novel production system concept based on MFCA analysis. By comparing these results, the
idea’s appropriateness could be quantitatively evaluated. In chapter 4, the procedure of considering new
process improvements based on MFCA analysis and the design and verification of the simulation is presented. The process improvement based on MFCA displays advantages over the traditional production
improvement concept. It is clearly demonstrated that this method is an effective decision-making tool.
978-1-4799-2076-1/13/$31.00 ©2013 IEEE
1973
Ichimura and Takakuwa
2
BRIEF LITERATURE REVIEW
Simulation has traditionally been used as a decision-making tool for the improvement of manufacturing
systems (Clymer 2000, Farahmand 2000). For many years, this approach has been regarded as a source of
competitiveness for enterprises in the manufacturing sector that desire to increase production efficiency
by eliminating waste according to the TPS (Monden 1998). This concept has spread outside Japan.
Known as the lean production system, TPS is recognized as a standard production management method.
However, the environmental impact of business activity has become a question of substantial interest to
society. Large firms, including Toyota, Panasonic and Suntory, have exerted significant environmental
management efforts (Kawaguchi 2011, Imamura 2011, Tomita 2011, Takaya 2011). There are many different approaches to environmental management. Several companies have introduced material flow cost
accounting (MFCA), for example, Sekisui Chemical Group, Canon and the Tanabe Seiyaku pharmaceutical company (Numata 2011, Environmental Industries Office 2010). MFCA is used not only to determine
losses but also indecision-making on capital investment, raw material procurement, product design, production planning and Kaizen activity (Kokubu 2002, Kokubu 2008). Although simulation research on
production management has been performed from the MFCA perspective, the number of such studies remains limited (Tang and Takakuwa 2011, Tang and Takakuwa 2012).
3
3.1
MFCA: BASIC DESCRIPTION AND CASE STUDY
MFCA
Material flow cost accounting (MFCA) is an environmental management accounting tool aimed to simultaneously reduce environmental impact and costs. It may be a decision-making tool for business executives and on-site managers. MFCA seeks to decrease costs through waste reduction, thereby improving
business and manufacturing productivity. MFCA measures the flow and the stock of “materials,” which
includes raw materials, parts and manufacturing process components, in terms of physical and monetary
units. The costs are managed using the categories of material cost, energy cost, system cost, and waste
treatment cost. The costs of losses incurred by defective products and waste and other emissions is identified through calculating their quantities and the resources used in each manufacturing process and converting the results into monetary values (Environmental Industries Office 2010). The MFCA concept is
shown in Figure 1.
Stock
sub materials
Stock
Main materials
Positive
product
Positive
Processing product
Process 1
Negative
products
Waste
Negative
products
Waste
・Waste due to
deterioration of
main materials, etc.
Stock
aux. materials
Positive
product
Stock
Work-in-process
Negative
products
Waste
Waste due to
・Waste due to
deterioration of stock
・Material loss,
・Defective processing, etc. Work-in-process, etc.
During the process
Positive
Processing product
Process 2
Negative
products
Waste
Stock
Products
Negative
products
Waste
Waste due to
・Waste due to
・Material loss,
deterioration of
stock products, etc.
・Defective products,
・Set-up loss
(remaining material,
adjustment),
・Auxiliary material loss, etc.
Figure 1: The MFCA concept (Environmental Industries Office 2007).
In the framework of MFCA, costs are calculated for good products and non-product output or material losses. Good products are usually called “positive products” and non-products or material losses are
1974
Ichimura and Takakuwa
called “negative products.” Costs of positive and negative products are categorized into four costs. Material costs are ones of material including main low material and sub material of additive, colorants, accessory and so on. System costs are ones of manufacturing system including labour and depreciation of
equipment. Energy costs are ones of fuel such as oil or gas, electricity power, compressed or vacuum air,
and water. Waste treatment costs are ones of detoxification.
In addition, in September 2011, ISO 14051 was published, and MFCA was internationally standardized. ISO 14051 clarifies the basic concept, the calculation method, and the implementation steps for
MFCA. The standard’s main purpose is to demonstrate the MFCA principle (Takakuwa et al. 2012).
3.2
Case Study
In this study, the research focus is an automobile parts manufacturer (Company T) located in Mie Prefecture, Japan. This company has developed and introduced an original production method developed from
TPS. The factory is efficiently operated such that the quantity of stock is maintained as low as possible.
The largest advantage of introducing MFCA at this factory has been the recognition of previously unnoticed losses and the determination of the amount of such losses. This factory produces automobile parts
by cutting, heating, forging and heat-treating iron bars as well as quenching, shot blasting and machining.
The company’s factory layout is shown in Figure 2. For each process, a substantial number of machines
are used. In this factory, secondary material, such as coolant, sand and oil, is used in each process. The
material and waste flow is shown in Figure 3.
Because the loss is calculated based on the weight of material in MFCA, the quantity center, which
represents the measured weight of the material, is important. There are four quantity centers, which
include raw material, WIP and waste as follows: (A) the cutting process, in which cut iron bar is
the primary material; (B and C) the heating and forging process, in which cut iron columns are heated,
forged and punched; (D and E) the heating treating and shot-blasting process, in which the surface is
hardened and the scaling is removed; and (F and G) the lathing and machining process, in which the machining of the finished product is performed. The reason why heating and forging are defined as belonging to the same quantity center is because these processes are performed using the same equipment line,
as are lathing and machining. The reason why the heating and treating processes are defined as belonging
to the same quantity center is because weight change does not occur and failed work pieces are not found
because there is no inspection process between heating treating and shot blasting.
Warehouse
Warehouse
(Steel raw material)
(Work in process after the cutting)
Circula
tion
Cutting
Facility
Compressor
room
Warehouse
Power
transformation
room
Check
(Final product)
Lathe And Machining Center Facility
Line-A
Warehouse
(Work in process before Lathe)
Line-B
Figure 2: Factory layout.
1975
Line-D
Line-C
Check
Line-N
Warehouse
Parings
place
S1
Shot
Blasting
Facility
Line-G1
Line-R1
Heavy
oil
tank
Line-R4
Line-R2
Line-Y
Line-R3
Heating
Forging
Facility
S2
Rest
station
FurnaceAII
Heavy oil tank
FurnaceAI
Heating
Treating
Facility
Parking
Meeting
room
Office
Dining room,
locker room
The office
headquarters
Ichimura and Takakuwa
Order
Steel
Coolant
1
Cutting
Process
2
Heating
Process
Scraps
Chips
Defective
3
Forging
Process
Oil
Sand
Coolant
Tools
Coolant
4
Heating
Treating
Process
5
Shot
Blasting
Process
6
Lathe
Process
7
M. C.
Process
Sand
Chips
Coolant
Defective
Chips
Coolant
Defective
Defective
Shipping
Figure 3: Material and waste flow at Company T.
3.3
MFCA Analysis
First, material and other costs were calculated by MFCA analysis as shown in Table 1. The material costs
an analysis is performed by inputting data such as the unit price of raw materials, the quantity of introduced raw materials, the number of positive (i.e., non-defective) products and negative products, such as
chips and defective products. The system cost is calculated based on the labour, tool and coolant costs,
among others, that are allocated to each process. Energy use, such as electric power, heavy oil, light oil
and LNG, as well as water use are similarly assessed by calculating the energy cost. As the result, the positive product cost was determined to be 71.4%, as shown in Figure 4. This outcome indicates that 28.6%
of the total input costs have not became product and have been discarded.
Table 1: Raw material data.
Input
Output
Intermediate product
Main material
Finished product (conforming)
Finished product (loss)
（kg）
（kg）
（kg）
（kg）
QC1
QC2
QC3
QC4
0.0 47,448.5 44,171.4 44,160.5
49,036.0 2,273.2
0.0 1,868.3
47,448.5 47,164.7 44,160.5 34,988.8
1,587.5 2,557.1
10.9 11,040.0
Figure 4: MFCA analysis results.
1976
Ichimura and Takakuwa
4
4.1
THE SIMULATION MODEL AS A DECISION-MAKING TOOL
The Search for an Optimal Solution from the MFCA perspective
A novel process improvement idea based on MFCA analysis is compared with conventional process improvement. Traditionally, TPS has been used in process improvement. TPS defines 7 categories of waste:
waste of defects, over-production, waiting, transportation, processing, inventory and motion. Increased
production efficiency by the elimination of waste is becoming a resource of growth potential in firms.
Particularly in recent years, inventories may require not only stock administration costs but also may
become dead stock because the product life cycle has become shorter. It has been a standard theory of
manufacturing to reduce inventory and production costs by the precise timing of sales. In fact, automobile
parts manufacturing Company T makes different shapes of the final product to reduce the amount of work
in process (WIP) inventory. The method used is to cut the product to its final shape in the machining center process after all of the parts have been prepared similarly. In the manufacturing sector, such approaches are referred to as formulas. As shown in Figure 5, this concept means that Part X is designed
to a size that is as close to the size of Part A as possible, and Part B is produced by taking advantage of
machining center allowances.
Part X
Punch out
Parts A
after machining
Parts B
after machining
Figure5: The concept of current relationship of the WIP and finished product (AS-IS model).
However, from the MFCA perspective, the magnitude of the negative product cost when producing
Part B increases the environmental impact because additional metal resources are required as raw material
and as a result of other associated costs, such as an increase in the amount of waste treatment, the number
of spent cutting tools, coolant, chips and energy loss.
This study proposes a procedure to determine the optimum process by calculating the costs and environmental impact while comparing the AS-IS and TO-BE models. The AS-IS model is the current production process, in which semi-finished products are produced until the midpoint of the production stream
is reached. The TO-BE model is the actual current production model in which the shape of the product is
as close to that of the final product as possible from the beginning of production stream (Figure 6). The
current process is known as the AS-IS model. Two types of final product (M1 and M2) are used to represent the machining center process, which is the end of the process in the AS-IS model. M1 and M2 are
manufactured as two varieties from the beginning in the TO-BE model. M1 involves a small amount of
cutting and M2 a large amount of cutting during the machining center process to produce the same part
that is produced from the cutting process to shot-blasting process in the AS-IS model. At Company T, the
1977
Ichimura and Takakuwa
cutting amount for M1 is 24%, whereas for M2, the amount is 38%. However, the negative cost generated
during the machining process is avoided in the TO-BE model because M1 and M2 are manufactured as
two varieties from the beginning.
Part X
Punch out
Part M1
after machining
Part Y
Punch out
Part M2
after machining
Figure 6: The concept of shaping the final product according to the upstream process (To-Be model).
In the TO-BE model, maintenance is required for each M1 and M2 WIP from the start of production.
It is assumed that WIP inventory maintenance costs increased by 20%. Various changes are simulated on
the condition that M1’s machining waste rate is the fixed-rate of 24%. In addition, in Table 2, it was assumed that the machining time shortened as the amount of cutting of M2 was decreased.
Table 2: The simulation conditions.
Models
AS-IS model
TO-BE model 1
TO-BE model 2
TO-BE model 3
TO-BE model 4
TO-BE model 5
TO-BE model 6
TO-BE model 7
TO-BE model 8
TO-BE model 9
4.2
Machining
waste rate
M1
M2
24% 38%
24% 37%
24% 36%
24% 35%
24% 34%
24% 33%
24% 32%
24% 31%
24% 30%
24% 29%
MC processing time
(Second)
TRIA (Min, Mode, Max)
Min
Mode
Max
414.0
444.0
445.0
413.3
434.2
435.1
412.6
442.5
443.5
411.9
441.7
442.7
411.2
441.0
442.0
410.5
440.2
441.2
409.8
439.5
440.5
409.1
438.7
439.7
408.4
438.0
438.9
407.7
437.2
438.2
Models
Machining
waste rate
M1
M2
TO-BE model 10
24% 28%
TO-BE model 11
24% 27%
TO-BE model 12
24% 26%
TO-BE model 13
24% 25%
TO-BE model 14
24% 24%
TO-BE model 15
24% 23%
TO-BE model 16
24% 22%
TO-BE model 17
24% 21%
TO-BE model 18
24% 20%
MC processing time
(Second)
TRIA (Min, Mode, Max)
Min
Mode
Max
407.0 436.4 437.4
406.2 435.7 436.7
405.5 434.9 435.9
404.8 434.2 435.1
404.1 433.4 434.4
403.4 432.7 433.6
402.7 431.9 432.9
402.0 431.1 432.1
401.3 430.4 431.4
Proposed Simulation Procedure
A simulation model based on the current manufacturing process was created to determine whether there is
any effect when the improvements generated by MFCA analysis are implemented. The simulation programs were written in ARENA. ARENA can handle continuous, discrete and mixed models and is particularly suitable for the simulation of discrete events that involve steps at discrete points in time. ARENA is
the most appropriate software for this type of system because the arrival time for the work, the processing
time and other events are retrieved as discrete information (Kelton, Sadwski and Swets 2010). Further1978
Ichimura and Takakuwa
more, ARENA has a satisfactory record in terms of empirical research on process improvement. Figure 7
shows the primary logic of the simulation model.
Figure 7: Primary logic of simulation model.
The primary logic is composed of 11 submodels, which include order arrivals, production plans, each
process and the parts departure. Production is started by the arrival of the order. Production plan is set up
by the kind of products. This plan will issue a production order to the respective processes. Production is
completed in all the products that will be shipped.
Figures 8 and 9 show the logic of the simulation submodel. Each step of the process occurs as a submodel, which combine to comprise the entire simulation model. The order number, delivery time and
quantity are defined by order information. It is planned that set up time and production time of each process, production lot number, the number of WIP and so on. Cutting and each process submodel is defined
amount of product weight, process time, default rate, waste rate, material cost, energy cost, recipe and so
on in each production process.
Order arrival submodel
Production plan submodel
Oder Arrivals
Order number
Order quantity
Production time of each process
Set up time of each process
To Production Plan
Submodel
Production Plan
Submodel
Assign
Production Plan Attribute
Production lot of each process
Assign
Production Plan
Attribute
Assign F
WIP 2
Assign F
WIP 3
Decide E
WIP
Assign E
WIP 1
Assign G
WIP 3
Assign E
WIP 2
Decide D
Production Lot
Number
Assign D
WIP 2
Decide B
Production Lot
Number
Assign D
WIP 3
Decide C
WIP
Assign E
WIP 3
Assign B
WIP 2
Assign A
WIP 1
Assign C
WIP 1
Assign C
WIP 2
Assign B
WIP 3
Decide A
WIP
Decide C
Production Lot
Number
Decide E
Production Lot
Number
Decide G
WIP
Assign G
WIP 2
Assign F
WIP 1
Decide G
Production Lot
Number
Assign
Attribute
Decide G
Production Lot
Number
Decide F WIP
Decide A
Production Lot
Number
Assign C
WIP 3
Assign A
WIP 2
Assign A
WIP 3
Decide B WIP
Decide D
WIP
Assign D
WIP 1
Assign B
WIP 1
To Cutting Submodel
Figure 8: Primary logic of simulation submodel of order arrival and production plan.
1979
Ichimura and Takakuwa
A: Cutting submodel
B-G: Process submodels
A:
A:Cutting
Cutting
Submodel
Submodel
B-G:
B-G:Process
Process
Submodel
Submodel
Seize
SeizeA
A
Processing
Processing
Decide
DecideBB
Parts
PartsType
Type11
Production lot of each process
Production lot of each process
Decide
DecideA
A
Parts
PartsType
Type
Decide
DecideA
A
Set
Setup
up
Batch
BatchM1
M1BB
Decide
DecideA
A
Parts
PartsType
Type
Batch
BatchM2
M2BB
Seize
SeizeBB
Processing
Processing
Delay
DelaySet
Setup
upA
A11
Assign
AssignA
A
MFCA
MFCA55
Delay
DelaySet
Setup
upA
A22
Assign
AssignA
A
MFCA
MFCA66
Decide
DecideBB
Parts
PartsType
Type55
Decide
DecideBB
Setup
Setup
Assign
AssignA
A
MFCA
MFCA44
Assign
AssignA
A
MFCA
MFCA33
Decide
DecideBB
Parts
PartsType
Type44
Assign
AssignA
A
Set
Setup
upattributes
attributes
Decide
DecideA
ANext
Next
Production
Productionlot
lot
Process
ProcessA
A
Delay
DelaySet
Setup
upBB11 Delay
DelaySet
Setup
upBB22
Calculate
CalculateA
A
Processing
Processing
Quantity
Quantity
Decide
DecideA
A
Parts
PartsType
Type
Decide
DecideA
A
Parts
PartsType
Type11
Assign
AssignM1
M1
A
AMFCA
MFCA11
Assign
AssignM2
M2
A
AMFCA
MFCA22
Reset
ResetA
A
Processing
Processing
Quantity
Quantity
Create
CreateM1
M1A
A
Initial
InitialWIP
WIP
Create
CreateM2
M2A
A
Initial
InitialWIP
WIP
Assign
AssignM1
M1A
A
Initial
InitialWIP
WIP
Attributes
Attributes
Assign
AssignM2
M2A
A
Initial
InitialWIP
WIP
Attributes
Attributes
Match
MatchM1
M1A
A
Assign
AssignM1
M1
BBMFCA
MFCA55
Assign
AssignM2
M2
BBMFCA
MFCA66
Assign
AssignM1
M1
BBMFCA
MFCA33
Assign
Assign B
B
Set
Set up
up Attributes
Attributes
Calculate
CalculateBB
Processing
Processing
Quantity
Quantity
Process
ProcessBB
Decide
DecideBBNext
Next
Production
Productionlot
lot
Assign
AssignM1
M1
BBMFCA
MFCA44
Reset
ResetBB
Processing
Processing
Quantity
Quantity
Match
MatchM2
M2A
A
Assign
AssignM1
M1
A
AMFCA
MFCA77
Decide
DecideBB
Parts
PartsType
Type22
Assign
AssignM2
M2
A
AMFCA
MFCA88
Discard
Discard
Assign
AssignM1
M1
BBMFCA
MFCA11
To
ToB:
B:Heating
HeatingProcess
Process
Parts departure submodel
Parts
PartsDeparture
Departure
Submodel
Submodel
Batch
BatchM1
M1C
C
Production
Production
Number
Number
Departure
Departure
Assign
AssignM2
M2
BBMFCA
MFCA22
Create
CreateM1
M1BB
Initial
InitialWIP
WIP
Create
CreateM2
M2BB
Initial
InitialWIP
WIP
Assign
AssignM1
M1BB
Initial
InitialWIP
WIP
Attributes
Attributes
Assign
AssignM2
M2BB
Initial
InitialWIP
WIP
Attributes
Attributes
Match
MatchM1
M1BB
Assign
AssignM1
M1
BBMFCA
MFCA77
Match
MatchM2
M2BB
Discard
Discard
Assign
AssignM2
M2
BBMFCA
MFCA88
To
ToParts
PartsDeparture
DepartureSubmodel
Submodel
Figure 9: Primary logic of simulation submodel of each process and parts departure.
1980
Ichimura and Takakuwa
4.3
Results
As shown in Figures 10 and 11, the total cost is reduced by decreasing the negative production cost. For
example, according to the simulation results, cutting waste and costs are reduced by increasing stock and
inventory management costs. The total cost is the lowest for TO-BE model 16, followed by TO-BE models 18 and 9. The waste rate of M2 during the machining process is 22%, 20% and 29%, respectively. The
decision to accept a 1.2 times increase in the inventory maintenance cost and a 22% waste rate for M2
were valid cost reduction measures. TO-BE model 16 results in a positive production cost rate increase of
approximately 3.5 points from the 77.13% of the AS-IS model to 80.62% (Figure 10). The results indicate
that decision-making based on a MFCA simulation was cost-effective. Decision-making based on the
MFCA perspective achieved cost reduction and improved environmental management according to the
method described above and the quantitative evaluation of the effects of MFCA on production.
Total cost (JPY1,000,000)
63.2
63.0
62.8
62.6
62.4
62.2
62.0
61.8
Positive production cost rate (%)
Figure 10: Total cost.
82
81
80
79
78
77
76
75
Figure 11: Positive production cost rate.
1981
Ichimura and Takakuwa
5
CONCLUSIONS
It was demonstrated that unrecognized loss can be revealed by introducing the MFCA method, which differs from the traditional TPS formulas. The insight that MFCA provided resulted in innovative suggestions for improving the manufacturing process. In addition, it has been demonstrated that the effectiveness
of traditional production methods, such as TPS, compared with that of new ideas based on MFCA could
be quantitatively evaluated using simulations, the procedure for which was described. The simulation results, particularly with respect to production costs, can be used advantageously in economic decisionmaking. The change of positive and negative costs determined using MFCA contributes significantly to
the execution of environmental management as an indicator of the effective utilization rate of resources
and the burden of waste and loss disposal. This proposed application is available for decision-making of
size of law material at the stage of product design, combination of variety for products at the stage of production planning and upgrading manufacturing. It may be usable for decision-making on alternatives of
building new factory in emerging countries or promote cost reduction in existing factory.
ACKNOWLEDGMENTS
The authors wish to express their sincere gratitude to Mr. Y. Watanabe of Metal Worker Toa Forging Co.,
Ltd. and Mr. Y. Murata of Fujita Health University, for their cooperation in completing this research. This
research was supported by the Grant-in-Aid of the Japan Society for the Promotion of Science (JSPS).
REFERENCES
Clymer, J. R. 2000. “Optimizing Production Work Flow Using OpEMCSS.” In Proceedings of the 2000
Winter Simulation Conference, Edited by J. A. Joines, R. R. Barton, K. Kang, and P. A. Fishwick,
1305–1314. Piscataway, New Jersey: Institute of Electrical and Electronics Engineers, Inc. ISBN: 0412-83930-X.
Environmental Industries Office, Industrial Science and Technology Policy and Environment Bureau,
Ministry of Economy, Trade and Industry of JAPAN. 2007. “Guide for Material Flow Cost Accounting (Ver.1).” http://www.meti.go.jp/policy/eco_business/pdf/mfca%20guide20070822.pdf
Environmental Industries Office, Industrial Science and Technology Policy and Environment Bureau,
Ministry of Economy, Trade and Industry of JAPAN. 2010. “Material Flow Cost Accounting MFCA
Case Examples. ”http://www.jmac.co.jp/mfca/thinking/data/MFCA_Case_example_e.pdf
Farahmand, K. 2000. “Using Simulation to Support Implementation of Flexible Manufacturing Cell.” In
Proceedings of the 2000 Winter Simulation Conference, Edited by J. A. Joines, R. R. Barton, K.
Kang, and P. A. Fishwick, 1272–1281. Piscataway, New Jersey: Institute of Electrical and Electronics
Engineers, Inc.
Imamura, S. 2011. “Environmental Management of Toyota Motor Corporation.” The Japan Society for
Quality Control 41(1): 23-28. (In Japanese)
Kawaguchi, T. 2011. “Toyota Environment Actions.” Japan Society for Information and Management
31(4): 55-62. (In Japanese)
Kelton, W. D., R. P. Sadowski, and N. B. Swets. 2010. Simulation with ARENA. 5th ed. New York:
McGraw-Hill, Inc.
Kokubu, K. 2008. Material Flow Cost Accounting. Tokyo: Japan Environmental Management Association For Industry (In Japanese)
Kokubu, K. 2010. “Production and Environment Management through Material Flow Cost Accounting.”
Japan Society for Information and Management 31(2): 4–10. (In Japanese)
Kokubu, K., and H. Tachikawa. 2012. “Material Flow Cost Accounting.” In Manufacturing and Environmental Management, Edited by S. Takakuwa. 265–283. Hanoi: National Political Publishing
House. ISBN: 987-604-57-0000-6.
1982
Ichimura and Takakuwa
Monden, Y. 1998. Toyota Production System. 3rd ed. Norcross, Georgia: Institute of Industrial Engineers.
Numata, M. 2011. “The Introduction of the Material Flow Cost Accounting in the Manufacturing Development Innovation of the Sekisui Chemical Group.” Japan Society for Information and Management
31(4): 83-88. (In Japanese)
Takaya, M. 2011. “’Suntory, Bringing Water to Life’ environment protection activities.” The Japan Society for Quality Control 41(1):13-18. (In Japanese)
Tang, X., and S. Takakuwa. 2011. “Simulation Analysis for ERP Conducted In Japanese SMEs Using
The Concept of MFCA.” In Proceedings of the 2011 Winter Simulation Conference, Edited by B. Johansson, S. Jain, J. Montoya-Torres, J. Hugan, and E. Yücesan, 2089–2100. Piscataway, New Jersey:
Institute of Electrical and Electronics Engineers, Inc.
Tang, X., and S. Takakuwa. 2012. “MFCA-Based Simulation Analysis for Environment-oriented SCM
Optimization Conducted by SMEs.” In Proceedings of the 2012 Winter Simulation Conference, Edited by C. Laroque, J. Himmelspach, R. Pasupathy, O. Rose, and A. M. Uhrmacher, Piscataway, New
Jersey: Institute of Electrical and Electronics Engineers, Inc.
Tomita, K. 2011. “The Panasonic’s Environmental Management of Panasonic.” The Japan Society for
Quality Control 41(1): 33-38. (In Japanese)
AUTHOR BIOGRAPHIES
HIKARU ICHIMURA is a Ph.D. student at the Graduate School of Economics and Business Administration, Nagoya University, Japan. He received his B.Sc. degree in engineering from Hokkaido University in 2002 and M.Sc. degree in economics from Nagoya University in 2012. His research interests include the improvement of the manufacturing process and the evaluation of environmental impact using
ARENA simulation and the Green Production Mode. His email address is [email protected].
SOEMON TAKAKUWA is a Professor at the Graduate School of Economics and Business Administration, Nagoya University, Japan. He received his B.Sc. and M.Sc. degrees in industrial engineering from
Nagoya Institute of Technology in 1975 and the Tokyo Institute of Technology in 1977, respectively. His
Ph.D. is in industrial engineering from Pennsylvania State University. He holds a doctorate in economics
from Nagoya University and a P.E. in Industrial Engineering. He is corresponding member of International Academy of Engineering in Russia. His research interests include the optimization of manufacturing and logistics systems, management information systems and simulation analysis of these systems in
the context of hospitals. He prepared the Japanese editions of the Introduction to simulation using
SIMAN and Simulation with ARENA. He serves concurrently as a senior staff member of the Department
of Hospital Management Strategy and Planning at Nagoya University Hospital. His email address is
[email protected].
1983